As advances in both computers and the Internet continue, the abundance of data and access to it can be overwhelming at times. While the ability to access this data is limited only by the computer one owns, turning it into useful knowledge is a very different problem. While more and more data is produced and available for use, processing methods which turn data into knowledge have lagged behind. Whole industries have developed centered around search engines simply to scour the internet for data on everything from apples to zoos and more. However even these methods fall short of providing knowledge, at best they return a listing of items that require Human decision making to refine the search further. Only after additional attempts at searching, each time refining the set of keywords or methods employed, does the searcher begin gain knowledge as to the most optimal method to obtain the information required.
In cases where data items are interrelated, the difficulty of transforming the data into useful knowledge increases significantly and is even a more difficult problem than above. In most cases when dealing with data, people tend to view data relationships one-dimensionally, most often as a listing of items, some examples include: hits for web pages based upon a set of keywords, a phonebook of names, product listings, US patents issued. In each of the above cases the data is categorized under similar groupings, similar data items are combined in the same group.
To move beyond the one-dimensional nature of utilizing data, traditional approaches employed to help one make sense of data for decision making have been to construct a model and then try to fit the data to the model. One of the more useful and successful models which accomplishes this is the simple organizational chart which shows individuals in an organization and their relationships in reporting structure. The additional dimension of reporting relationships transforms the one-dimensional list of individuals to a very useful image which represents not only the individuals but the relationships of the individuals to each other. An additional example of a two dimensional data relationship would be airline routes which show the originating and destination points connected by a curved line which represents the flight connecting those two cities. But data can have multiple relationships both within similar data sets and dissimilar data sets. Building models for these situations in order to transform the data into knowledge useful for high-level and strategic decision making is significantly more difficult. In these cases one needs to know the landscape, be part of it, until now this was difficult at best for the three-dimensional case and nonexistent for dimensions beyond.
With the widespread availability of data and more powerful computer technology, being successful at defining relationships of voluminous complex interrelated data on multiple levels in a timely manner is now possible with the method/software disclosed. The software system described herein organizes, analyzes and presents in an optimized fashion a comprehendible graphic representation of the available data allowing the user to immediately and intrinsically infer the existence of relationships and trends that would normally not have been apparent otherwise. This new method supports decision making to a level never achieved before and is capable of presenting data relationships across multiple planes and accessing dissimilar data sets.
The FRIDAY system is such a decision support system (Find, Relate, Infer, Discover, Analyze, and Yes to actions) For example, if a decision has to be made to invest in a particular opportunity/technology, FRIDAY can greatly enhance the evaluation of intellectual property that surrounds the technology that exists in patent, published paper, and prior art form. FRIDAY can also find business data and the relationships that exist relevant to the opportunity, relate that to the intellectual property data and then to other relevant facts (such as market issues, competition, economic activity, etc.). Having developed the ‘landscape’ for this particular technology the system can then infer specific important trends that have direct relevance to the opportunity.
From a programmer's perspective, the FRIDAY system is a collection of targeted databases with powerful data retrieval, data correlation and data connection attributes, and unique inference and business communication capabilities. From a user's perspective FRIDAY is a user-friendly system that can sort, analyze, and make sense of data from many different expertise areas and then correlate this data to help illustrate relationships and graphically represent trends of importance that can help in drawing conclusions or supporting actions.
As an example, the existence of patents is becoming increasingly important as businesses place increasingly more value on intellectual capital. Most companies pursue patents to maintain or improve their competitive position. However the standard benchmark for most if not all of these companies is simply the number of patents possessed, a one-dimensional measure. Some of the more progressive companies may go a step further a begin to utilize additional information such as who are the most prolific inventors in order to determine their key contributors to the organization, which is a different measure but still nevertheless one-dimensional. Some two-dimensional measures that companies are beginning to tap is in the area of licensing opportunities. An example of this is IBM approach, IBM possessor of numerous patents, has recognized that the patents owned by them (one-dimensional) when assessed against others patents may yield connections resulting in patents which incorporate a significant portion of the art represented in IBM owned patents, which to IBM can equate to significant licensing opportunities. While our software system can accomplish this with algorithms more effectively and efficiently, it can go beyond and relate other data items such as the company's products related to these patents and/or the inventors associated with the patents and the companies these inventors were employed at before their present company and the patents that were assigned to these companies.
There are also other areas where this software can be applied, as an example tracking major sports figures and the teams they played on over the life of their career is an example of a three-dimensional mapping easily performed by our system, adding their salaries at each point in time is also possible, or for that matter any of their stats.
This software system can also be applicable in the financial sector, where many financial measures can be combined to illustrate trends and relationships that would normally not be apparent by looking at them individually.
To the authors knowledge there is not another system that currently attempts to coherently correlate data from an arbitrary set of separate domains in such an optimized graphical representation.